Electric public utilities normally provide energy to telecommunication networks. Historically, the traditional wire line telephony networks have been required to have extremely high reliability levels (between 99.999% and 99.99999%) to handle lifeline services such as 911 and alarm systems, while the electric utilities only offer a 99.9% reliability level. It is therefore important and considered best practice for telephone companies to have 8 hours of standby energy to power their network equipment in the event of a power outage. More recent telecommunication technologies, such as wireless and broadband, are also moving towards a high level of network reliability.
Batteries for power sources are usually provided in banks or strings, for example, a string of 24 batteries is often used for back-up purposes in central offices of telecommunications providers and in remote locations of transmission stations. These backup battery power systems provide the energy to power equipment in the event of an electrical outage or failure. Therefore, maintaining the reliability of battery power systems, especially backup battery power systems, is extremely important. Further, it is important to be able to predict the level of power needed in case of power outages or failures and plan where extra batteries may be needed.
For the last century, operators and equipment suppliers have struggled to offset the costs and risks associated with battery reliability in the hostile remote environment. Much has been invested in lead-acid battery design effort, in charging system design, and in monitoring and prediction algorithm technology to overcome the problems associated with lead-acid batteries. It is now recognized that lead-acid batteries have reached the maximum performance attainable by their electrochemical system, and that fundamental issues related to the unpredictability of their end-of-life coupled with their short life under field conditions are not going to be resolved.
End-users, either in the telecommunication industry or in other industries having similar requirements, have a need to maintain reliability at required levels. This need cannot be filled when using lead-acid batteries because of the unpredictable nature of their electrochemical system. Its is almost impossible to accurately determine their State-Of-Charge (SOC) or State-Of-Health (SOH) over the life of lead-acid batteries. Typically, the only time at which end-users have an accurate measure of the batteries' SOC and SOH is during installation of brand new strings of batteries. As soon as the batteries have been exposed to field conditions, end-users cannot dynamically determine the battery's SOC and SOH except by performing a deep battery discharge, which affect the SOC and SOH and requires every equipment site to be visited by end-users. Furthermore, the reserve time required at each equipment site (typically 8 hours) cannot be estimated or calculated due to the lead-acid unpredictability and the fact that electrical load cannot be monitored, calculated or integrated to the battery system.
Traditional maintenance of lead-acid battery strings in the telecommunications industry has focused on a series of routines mandating periodic measurements of battery parameters, such as cell voltage and specific gravity. It was thought that if batteries were physically maintained with proper water levels, visual inspections, and correct voltage and specific gravity readings, the batteries would provide the necessary capacity when needed. However, when forced on-line, batteries often failed or produced far less than stated capacity even if they were properly maintained. It is now well-settled that these types of measurements are not accurate predictors of battery capacity.
Various systems and methods have been devised to predict or monitor State-Of-Charge of lead-acid batteries over their life span. For instance, U.S. Pat. No. 6,211,654 discloses iterative calculations based on voltage readings at specific intervals to estimate the remaining back-up time or current discharge capability of a lead-acid battery. The method disclosed provides only a rough estimate of the back-up time and does not take into account temperature variables, specific loads of the equipment, and battery age and/or deterioration.
Lithium Polymer (LP) batteries on the other hand have relatively high density energy (high energy generation in a low volume package), relatively high safety margins, and produce energy from a highly predictable electrochemical system. Lithium Polymer batteries are equipped with on-board control and monitoring integrated electronics able to accurately measure each battery's SOH and SOC individually taking into account temperature variables.
More advanced systems and methods were devised for non-specific types of batteries to monitor a battery back-up power system. For instance, U.S. Pat. No. 5,705,929 discloses a method and apparatus for centrally monitoring the capacity of batteries in a battery string including electrical leads connected to each battery terminal of the battery string. A capacity testing system a) switches between the electrical leads for sequentially selecting the leads associated with the terminals of each battery, b) measures the internal resistance of the battery associated with each selected pair of electrical leads, c) compares the internal resistance of each battery cell to an internal resistance threshold, and d) triggers an alarm when the internal resistance of a battery exceeds the internal resistance threshold. A central monitoring station monitors battery capacity data and alarm signals from various battery strings, schedules battery capacity testing, transmits control commands to each capacity testing system for i) scheduling testing, ii) initialising upload of capacity data, and iii) requesting status information, provides battery capacity data analysis, and uploads information to a network management computer. This system is an improvement over the previous manual testing procedures however it falls short in that it can only determine the apparent State of Health of the battery power system as good or not good, detecting malfunctions of the batteries (alarms) and relaying the alarms to a central monitoring system. This system is unable to accurately predict battery back-up time based on real time data. When an actual power outage occurs, the end user is left hoping that the back-up system will last.
Furthermore, when testing batteries to evaluate their capacity or state of health, most systems and apparatus known draw current from the batteries by placing a resistive load at the battery terminals for a short period of time. This leads to energy waste as the batteries must be recharged.
To fulfill the requirements of the telecommunications industry, and other critical industries using battery packs as back-up power systems when electric public utilities fail, there is a need for a reliable monitoring system that accurately predicts battery back-up time based on real time data and on changing equipment load.